Cryogenic storage tank temperature field monitoring and pressure relief period prediction method, device and equipment

Abstract

The invention discloses a cryogenic storage tank temperature field monitoring and pressure relief period prediction method, device and equipment, and relates to the technical field of cryogenic storage tank safety monitoring. The method comprises the steps that a temperature field reduced-order model and a heat flow reduced-order model are generated through simulation calculation and fitting, and if the temperature field needs to be monitored and the pressure relief period needs to be predicted, the temperature field reduced-order model and the heat flow reduced-order model are calculated; if yes, the real-time monitoring values of the liquid phase height, the liquid phase temperature, the gas phase temperature and the outer wall surface temperature of the cryogenic storage tank serve as input, the real-time temperature field of the cryogenic storage tank is determined through a temperature field reduced-order model, and the real-time outer wall surface total heat flow of the cryogenic storage tank is determined through a heat flow reduced-order model; based on the real-time monitoring value of the gas phase pressure of the cryogenic storage tank and the real-time total heat flow of the outer wall face, the real-time pressure relief period of the cryogenic storage tank is calculated, the temperature field can be monitored more accurately in real time, the pressure relief period can be predicted more accurately in real time, and the safety and operation efficiency of the cryogenic storage tank are effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of cryogenic storage tank safety monitoring, and particularly to a method, device and equipment for monitoring the temperature field of a cryogenic storage tank and predicting the pressure relief cycle. Background Art

[0002] Cryogenic storage tanks are important equipment for storing cryogenic liquids such as liquid oxygen and liquid nitrogen, and are widely used in industries, medical treatment, scientific research and other fields. Due to the limited thermal insulation performance of cryogenic storage tanks, there is a heat leakage phenomenon, resulting in liquid evaporation and pressure rise. To ensure safety, cryogenic storage tanks need to be regularly depressurized, but too long or too short pressure relief cycles will have adverse effects. In addition, uneven temperature fields can cause brittle fracture or deformation of the tank body material due to thermal stress. Therefore, it is of great significance to monitor the temperature field of cryogenic storage tanks and predict the pressure relief cycle of cryogenic storage tanks.

[0003] In related technologies, numerical simulation methods are mostly used to monitor the temperature field of cryogenic storage tanks, but numerical simulation methods are time-consuming and have poor real-time performance. Empirical formula methods are mostly used to predict the pressure relief cycle of cryogenic storage tanks, but the accuracy of empirical formula methods is not high. Summary of the Invention

[0004] The purpose of the present application is to provide a method, device and equipment for monitoring the temperature field of a cryogenic storage tank and predicting the pressure relief cycle, which overcomes the disadvantages of time-consuming numerical simulation methods, poor real-time performance and low accuracy of empirical formula methods, and can more real-time and accurately monitor the temperature field and predict the pressure relief cycle, effectively improving the safety and operation efficiency of cryogenic storage tanks.

[0005] To achieve the above purpose, the present application provides the following solutions.

[0006] In the first aspect, the present application provides a method for monitoring the temperature field of a cryogenic storage tank and predicting the pressure relief cycle, and the method for monitoring the temperature field of a cryogenic storage tank and predicting the pressure relief cycle includes the following steps.

[0007] Obtain the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature, outer wall temperature and gas phase pressure of the cryogenic storage tank.

[0008] Taking the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature and outer wall temperature of the cryogenic storage tank as inputs, use the temperature field reduced-order model to determine the real-time temperature field of the cryogenic storage tank, and use the heat flux reduced-order model to determine the total real-time heat flux of the outer wall of the cryogenic storage tank.

[0009] Based on the real-time monitoring value of the gas phase pressure of the cryogenic storage tank and the total real-time heat flux of the outer wall, calculate the real-time pressure relief cycle of the cryogenic storage tank.

[0010] Among them, the establishment process of the temperature field reduced-order model and the heat flux reduced-order model includes: constructing a thermal simulation geometric parameterization model of the cryogenic storage tank; for each set of preset sample parameters, setting the boundary conditions of the thermal simulation geometric parameterization model based on the sample parameters to obtain a thermal simulation parameterization model, and performing simulation calculations based on the thermal simulation parameterization model to obtain the sample temperature field and the total heat flux of the outer wall surface of the cryogenic storage tank corresponding to the sample parameters; fitting all sets of the sample parameters and the sample temperature fields corresponding to each set of the sample parameters to generate the temperature field reduced-order model, and fitting all sets of the sample parameters and the total heat flux of the outer wall surface corresponding to each set of the sample parameters to generate the heat flux reduced-order model; the sample parameters include the sample values of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank.

[0011] In a second aspect, the present application provides a device for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank. The device for monitoring the temperature field and predicting the pressure relief cycle of the cryogenic storage tank includes: a processor and a liquid level sensor, a first temperature sensor, a second temperature sensor, a third temperature sensor, and a pressure sensor communicatively connected to the processor.

[0012] The liquid level sensor, the first temperature sensor, the second temperature sensor, the third temperature sensor, and the pressure sensor are all installed on the cryogenic storage tank. The liquid level sensor is used to collect a real-time monitoring value of the liquid phase height of the cryogenic storage tank. The first temperature sensor is used to collect a real-time monitoring value of the liquid phase temperature of the cryogenic storage tank. The second temperature sensor is used to collect a real-time monitoring value of the gas phase temperature of the cryogenic storage tank. The third temperature sensor is used to collect a real-time monitoring value of the outer wall surface temperature of the cryogenic storage tank. The pressure sensor is used to collect a real-time monitoring value of the gas phase pressure of the cryogenic storage tank.

[0013] The processor is used to obtain the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature, outer wall surface temperature, and gas phase pressure of the cryogenic storage tank, execute the above-mentioned method for monitoring the temperature field and predicting the pressure relief cycle of the cryogenic storage tank, and determine the real-time temperature field and the real-time pressure relief cycle of the cryogenic storage tank.

[0014] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned method for monitoring the temperature field and predicting the pressure relief cycle of the cryogenic storage tank.

[0015] According to the specific embodiments provided by the present application, the present application has the following technical effects.

[0016] The present application provides a method, device and equipment for monitoring the temperature field of a cryogenic storage tank and predicting the pressure relief cycle. First, a thermal simulation geometric parameterization model of the cryogenic storage tank is constructed. For each set of preset sample parameters (including the sample values of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature of the cryogenic storage tank), the boundary conditions of the thermal simulation geometric parameterization model are set based on the sample parameters to obtain a thermal simulation parameterization model. Then, simulation calculations are performed based on the thermal simulation parameterization model to obtain the sample temperature field and the total heat flux of the sample outer wall surface corresponding to the sample parameters. The temperature field reduced-order model is generated by fitting all sets of sample parameters and the sample temperature fields corresponding to each set of sample parameters, and the heat flux reduced-order model is generated by fitting all sets of sample parameters and the total heat flux of the sample outer wall surface corresponding to each set of sample parameters. After obtaining the temperature field reduced-order model and the heat flux reduced-order model, if it is necessary to monitor the temperature field and predict the pressure relief cycle, the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature, outer wall temperature, and gas phase pressure of the cryogenic storage tank can be obtained first. Using the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature of the cryogenic storage tank as inputs, the real-time temperature field of the cryogenic storage tank is determined using the temperature field reduced-order model, and the total heat flux of the real-time outer wall surface of the cryogenic storage tank is determined using the heat flux reduced-order model. Based on the real-time monitoring value of the gas phase pressure of the cryogenic storage tank and the total heat flux of the real-time outer wall surface, the real-time pressure relief cycle of the cryogenic storage tank is calculated. When monitoring the temperature field, numerical simulation is not required, solving the problems of long time consumption and poor real-time performance existing in the numerical simulation method, and monitoring the temperature field more real-time. When predicting the pressure relief cycle, the real-time outer wall heat flux can be determined first, and then the real-time pressure relief cycle can be further calculated. It is not necessary to rely entirely on empirical formulas for prediction, solving the problem of low accuracy existing in the empirical formula method, and predicting the pressure relief cycle more accurately. Since it can monitor the temperature field and predict the pressure relief cycle more real-time and accurately, it can avoid the problems that the uneven temperature field may cause brittle fracture or deformation of the tank body material due to thermal stress, and at the same time avoid the adverse effects caused by too long or too short pressure relief cycles, and can effectively improve the safety and operation efficiency of the cryogenic storage tank. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is an application environment diagram of a method for monitoring the temperature field of a cryogenic storage tank and predicting the pressure relief cycle provided in Embodiment 1 of the present application.

[0019] Figure 2 Schematic flow chart of a cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method provided in Embodiment 1 of the present application.

[0020] Figure 3 Schematic diagram of the digital twin technology route of a cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method provided in Embodiment 1 of the present application.

[0021] Figure 4 Schematic diagram of the geometric model of the cryogenic storage tank provided in Embodiment 1 of the present application.

[0022] Figure 5 Schematic diagram of the geometric parameters of the cryogenic storage tank provided in Embodiment 1 of the present application.

[0023] Figure 6 Schematic diagram of the digital twin model for cryogenic storage tank temperature field monitoring provided in Embodiment 1 of the present application.

[0024] Figure 7 Schematic diagram of the digital twin model for cryogenic storage tank pressure relief cycle prediction provided in Embodiment 1 of the present application.

[0025] Figure 8 Schematic diagram of the structure of a computer device provided in Embodiment 3 of the present application.

[0026] Reference numerals: 1 - outer tank model; 2 - inner tank model; 3 - vacuum interlayer model. Detailed implementation manners

[0027] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0028] Embodiment 1.

[0029] The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method provided in the embodiments of the present application can be applied to, for example Figure 1In the application environment shown. Among them, the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be set separately, integrated on the server, placed in the cloud or on other servers. The terminal can send the monitoring and prediction requests to be processed to the server. After receiving the monitoring and prediction requests to be processed, for the monitoring and prediction requests to be processed, the server obtains the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature, outer wall temperature and gas phase pressure of the cryogenic storage tank; using the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature and outer wall temperature of the cryogenic storage tank as inputs, the server determines the real-time temperature field of the cryogenic storage tank using the temperature field reduction model, and determines the total real-time outer wall heat flux of the cryogenic storage tank using the heat flux reduction model; based on the real-time monitoring value of the gas phase pressure of the cryogenic storage tank and the total real-time outer wall heat flux, the server calculates the real-time pressure relief period of the cryogenic storage tank. The server can feedback the monitoring result of the real-time temperature field and the total real-time outer wall heat flux for the monitoring and prediction requests and the prediction result of the real-time pressure relief period to the terminal.

[0030] In addition, in some embodiments, the method for monitoring the temperature field and predicting the pressure relief period of the cryogenic storage tank can also be implemented by the server or the terminal alone. For example, the terminal can directly process the monitoring and prediction requests to be processed, or the server can obtain the monitoring and prediction requests to be processed from the data storage system and process the monitoring and prediction requests to be processed.

[0031] Among them, the terminal can be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0032] In an exemplary embodiment, as Figure 2 shown, a method for monitoring the temperature field and predicting the pressure relief period of a cryogenic storage tank is provided. This method is executed by a computer device, and can be specifically executed by a computer device such as a terminal or a server alone, or jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to Figure 1 the server in as an example for illustration, the method includes the following steps.

[0033] Step S1, obtain the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature, outer wall temperature and gas phase pressure of the cryogenic storage tank.

[0034] Step S2: Using the real-time monitoring values of the liquid level height, liquid temperature, gas temperature, and outer wall temperature of the cryogenic storage tank as inputs, determine the real-time temperature field of the cryogenic storage tank using the temperature field reduced-order model, and determine the total real-time heat flux of the outer wall of the cryogenic storage tank using the heat flux reduced-order model.

[0035] Step S3: Based on the real-time monitoring value of the gas pressure of the cryogenic storage tank and the total real-time heat flux of the outer wall, calculate the real-time pressure relief period of the cryogenic storage tank.

[0036] By implementing the above Steps S1 to S3, this embodiment overcomes the disadvantages of the numerical simulation method such as long time consumption, poor real-time performance, and low accuracy of the empirical formula method, and monitors the temperature field and predicts the pressure relief period more real-time and accurately, effectively improving the safety and operation efficiency of the cryogenic storage tank.

[0037] With the development of digital twin technology, it provides a new solution for the temperature field monitoring and pressure relief period prediction of cryogenic storage tanks. The following will introduce in detail the digital twin-based temperature field monitoring and pressure relief period prediction method used in this embodiment, including the following steps. Figure 3 A detailed introduction is given to the method for monitoring the temperature field and predicting the pressure relief period of the cryogenic storage tank based on digital twin used in this embodiment, including the following steps.

[0038] (1) Establish a thermal simulation parametric model of the cryogenic storage tank, generate training samples (including temperature field sample data and heat flux sample data) using DOE (Design of Experiments), establish a temperature field reduced-order model using the static reduced-order model method, establish a heat flux reduced-order model using the response surface method, reduce the computational complexity, and connect different modules to build a digital twin model system.

[0039] (1) Thermal simulation parametric model.

[0040] (1.1) Geometric model.

[0041] In this embodiment, based on the actual structure of the cryogenic storage tank, a geometric model of the cryogenic storage tank is constructed. The geometric model consists of three parts, namely an inner tank model, an outer tank model, and a vacuum interlayer model. The vacuum interlayer model is located between the inner tank model and the outer tank model. The inner tank model stores liquid (i.e., liquid phase) and gas (i.e., gas phase), and the vacuum interlayer model is simplified into an isotropic heat-conducting material.

[0042] Preferably, according to the geometric characteristics, physical properties, and simulation analysis types of the cryogenic storage tank, the geometric model of the cryogenic storage tank is reasonably simplified. Since the cryogenic storage tank is a symmetric structure, in order to simplify the geometric model, only a 1 / 4 geometric model of the cryogenic storage tank is established for finite element simulation analysis. The model simplification is specifically as follows: The geometric model of the cryogenic storage tank is a 1 / 4 geometric model of the cryogenic storage tank divided by the symmetry plane, and the geometric model is as Figure 4 shown Figure 4Among them, 1 is the outer tank model, 2 is the inner tank model, and 3 is the vacuum interlayer model.

[0043] (1.2)Model parameterization.

[0044] Parametric modeling can achieve rapid modeling and batch simulation calculations, improving efficiency. At the same time, parameterization of input and output is also required to generate reduced-order models of temperature fields and heat fluxes. Therefore, parametric modeling is adopted to parameterize the geometric parameters, material parameters, and boundary conditions of the geometric model of the cryogenic storage tank, realizing rapid modeling and batch simulation calculations and improving efficiency.

[0045] (1.2.1)Geometric parameterization.

[0046] In this embodiment, the geometric parameters and material parameters of the geometric model are set for geometric parameterization to obtain a parametric model of the thermal simulation of the cryogenic storage tank. As Figure 5 shown, the geometric parameters include the thickness, length, and inner diameter of the inner tank model, as well as the thickness, length, and outer diameter of the outer tank model. The material parameters include the first thermal conductivity of the inner tank model, the second thermal conductivity of the outer tank model, and the third thermal conductivity of the vacuum interlayer model.

[0047] The thermal conductivities of the inner tank model and the outer tank model adopt the thermal conductivities of the materials of the tank body itself. The thermal conductivity of the vacuum interlayer model adopts the apparent thermal conductivity. Specifically, the daily evaporation rate of the cryogenic storage tank at a filling rate of 90% is obtained through experiments. Further, the total heat flux on the outer wall surface is calculated through the daily evaporation rate and the latent heat of vaporization of the medium (i.e., the liquid in the cryogenic storage tank, generally cryogenic liquids such as liquid oxygen and liquid nitrogen). By continuously adjusting the apparent thermal conductivity of the vacuum interlayer model and performing simulation calculations until the total heat flux on the outer wall surface obtained from the simulation calculation is equal to or close to (i.e., the difference is less than the preset difference) the total heat flux on the outer wall surface obtained from the experiment, the apparent thermal conductivity of the vacuum interlayer model at this time is used as the thermal conductivity of the vacuum interlayer model during simulation.

[0048] (1.2.2)Boundary condition parameterization.

[0049] The boundary conditions and the simulation calculation results are parameterized respectively as the input parameters and output parameters of the training samples for generating reduced-order models of temperature fields and heat fluxes.

[0050] When parameterizing the boundary conditions, the required data includes: liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature. At this time, the data that needs to be obtained in this embodiment includes: liquid phase height, liquid phase temperature, gas phase temperature, outer wall temperature, inner tank length, inner tank inner diameter, inner tank thickness, outer tank length, outer tank outer diameter, and outer tank thickness. The value ranges of the relevant parameters are shown in Table 1. Among them, the value range of the liquid phase height can specifically be 0 - 2540 mm, the value range of the liquid phase temperature can specifically be -196°C - 0°C, the value range of the gas phase temperature can specifically be -196°C - 0°C, and the value range of the outer wall temperature can specifically be -40°C - 40°C.

[0051] Table 1 Parameter Value Ranges

[0052] The boundary conditions are specifically set as follows: the temperature of the outer surface of the outer tank of the cryogenic storage tank is set to the outer wall temperature (or ambient temperature), the temperature of the surface of the inner tank in contact with the liquid is set to the liquid phase temperature, and the temperature of the surface of the inner tank in contact with the gas is set to the gas phase temperature.

[0053] After completing the boundary condition setting, a parametric thermal simulation model of the cryogenic storage tank can be generated. Based on the parametric thermal simulation model, simulation calculations are carried out. The obtained simulation calculation results can include temperature distribution (i.e., temperature field), total heat flux on the outer wall surface, and heat flow conditions. Subsequently, the evaporation rate of the liquid can be calculated through the total heat flux on the outer wall surface of the cryogenic storage tank and the latent heat of vaporization of the medium, thereby the daily evaporation rate of the cryogenic storage tank can be calculated, and further the pressure relief period of the cryogenic storage tank can be predicted.

[0054] Here, the input parameters and output parameters of the training samples are described: For the temperature field reduction model, the training samples are the temperature field sample data. The input parameters of the temperature field sample data are liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature, and the output parameter is the temperature field. For the heat flux reduction model, the training samples are the heat flux sample data. The input parameters of the heat flux sample data are liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature, and the output parameter is the total heat flux on the outer wall surface.

[0055] (2) Generate training samples.

[0056] In this embodiment, DOE experiments are used to generate training samples, which include temperature field sample data and heat flux sample data. Subsequently, based on the temperature field sample data, a static reduced-order model method is adopted to establish a temperature field reduced-order model, and based on the heat flux sample data, a response surface method is adopted to establish a heat flux reduced-order model, reducing the computational complexity. Specifically, through DOE experiments, the value-taking of the boundary conditions of the thermal simulation parametric model of the cryogenic storage tank is reasonably designed to generate multiple thermal simulation parametric models. Each value-taking of the boundary conditions corresponds to a thermal simulation parametric model, and then batch simulation calculations are carried out to quickly obtain enough training samples required for generating the temperature field reduced-order model and the heat flux reduced-order model. Then, artificial intelligence learning (such as machine learning) is used to process the training samples to obtain the temperature field reduced-order model and the heat flux reduced-order model, reducing the computational complexity and significantly reducing the computational time. Specifically, a static reduced-order model method is adopted to establish the temperature field reduced-order model, and a response surface method is adopted to establish the heat flux reduced-order model.

[0057] The DOE experiment uses the Latin Hypercube Sampling Design method to design and generate multiple groups of sample parameters. The Latin Hypercube Sampling Design method is a method for approximately randomly sampling from a multivariate parameter distribution and belongs to the stratified sampling technique. Each group of sample parameters includes sample values of liquid height, liquid temperature, gas temperature, and outer wall temperature. The multiple groups of sample parameters are sorted out and input into an EXCEL table, saved in CSV format, and used as the input parameter data of the training samples for the temperature field reduced-order model and the heat flux reduced-order model. As an example, the DOE experiment can be designed using the ResponseSurface module of ANSYS to establish multiple groups of sample parameters.

[0058] For each group of sample parameters, the boundary conditions of the thermal simulation parametric model of the cryogenic storage tank are set based on the sample parameters to generate a thermal simulation parametric model, and simulation calculations are carried out using the thermal simulation parametric model to obtain the sample temperature field and the total heat flux of the sample outer wall corresponding to each group of sample parameters. Similarly, the sample temperature field and the total heat flux of the sample outer wall corresponding to each group of sample parameters are sorted out and input into an EXCEL table, saved in CSV format, and used as the output parameter data of the training samples for the temperature field reduced-order model and the heat flux reduced-order model, thus completing the generation of the training samples.

[0059] In this embodiment, the generation of the training samples can be completed using simulation software. As an example, the generation of the training samples can be assisted by the StaticROMPreprocesing plug-in of ANSYS. Insert the StaticROM Pre item in the Solution, set its properties, select the liquid height, liquid temperature, gas temperature, and outer wall temperature as the input parameters, and select the temperature field or the total heat flux of the outer wall as the output parameter.

[0060] Specifically, for the temperature field sample data, in ANSYS, the generation of the temperature field sample data requires the assistance of the StaticROMPreprocesing plug-in. Therefore, before generating the temperature field sample data, the StaticROMPreprocesing plug-in should first be installed in ANSYS Workbench. Then, in the Mechanical module, parameterize the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature in the boundary conditions, as well as the maximum, average, and minimum values in the temperature field. Finally, insert the StaticROM Pre item in the Solution, and set the file directory, input parameters, and output parameters in the property card of the StaticROM Pre item. Select the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature as the input parameters, and select the temperature field as the output parameter. After running the calculation, temperature field sample data in a certain format will be generated in the set file directory.

[0061] (3) Establish a digital twin model system.

[0062] (3.1) Generate a reduced-order model of the temperature field.

[0063] The reduced-order model of the temperature field is used to monitor the overall temperature conditions of the cryogenic storage tank, including the lowest temperature, the highest temperature, and the average temperature.

[0064] To generate the reduced-order model of the temperature field, a certain number of temperature field sample data need to be prepared in a certain format, and then the temperature field sample data are processed using artificial intelligence learning to generate the reduced-order model of the temperature field. After obtaining the temperature field sample data, the reduced-order model of the temperature field can be established using the static reduced-order model method.

[0065] The generation of the reduced-order model of the temperature field can be completed using simulation software. As an example, the generation of the reduced-order model of the temperature field can be assisted by the ANSYS Twin Builder software. The ANSYS Twin Builder software is included in the ANSYS Electronics software package. Therefore, it is necessary to first install ANSYS Electronics, then import the temperature field sample data into the Static ROM Builder module in ANSYS Twin Builder. In the Build tab, select the temperature field sample data, perform the reduced-order calculation to generate the reduced-order model of the temperature field. Finally, in the Evaluate tab, save the reduced-order model of the temperature field and output the reduced-order model of the temperature field to ANSYS Twin Builder.

[0066] Since ANSYS Twin Builder is a system simulation software, this type of software is usually compatible with the FMI (Functional Mockup Interface) model interface specification. In the project management window of ANSYS Twin Builder, find the Generate Rom Models item, right-click to export the FMU (Functional Mockup Unit) model, and then export the temperature field reduced-order model as an FMU model. The FMU model complies with the FMI interface specification and can be used in other system simulation software, which is beneficial to the subsequent construction of the digital twin model system in Simulink in this embodiment.

[0067] (3.2) Generate a reduced-order model of heat flow.

[0068] To generate a heat flow reduction model, it is necessary to prepare a certain amount of heat flow sample data in a certain format, and then use artificial intelligence learning to process the heat flow sample data to generate a heat flow reduction model. After obtaining the heat flow sample data, the response surface method can be used to establish the heat flow reduction model.

[0069] The generation of the heat flow reduced-order model can be completed by using simulation software. As an example, the generation of the heat flow reduced-order model can be achieved by using the Response Surface ROM module in ANSYS Twin Builder. The heat flow sample data is imported into the ResponseSurface ROM module to generate the heat flow reduced-order model.

[0070] The Response Surface ROM module generates a heat flow reduction model based on the response surface theory (i.e., the response surface method). The basic principle of the response surface theory is to use an approximate function that is easy to calculate and easy to approximate the real response function to approximate the real response function. The approximate function generally uses a polynomial function. By selecting a certain number of heat flow sample data and using iterative calculation to obtain the approximate function, the more heat flow sample data, the more accurate the approximate function, and the more heat flow sample data, the greater the calculation cost. In actual calculations, a certain number of heat flow sample data should be reasonably selected.

[0071] Since ANSYS Twin Builder belongs to system simulation software, such software usually complies with the FMI model interface specification. In the project management window of ANSYS Twin Builder, find the item of generating Rom Models, right-click to export the FMU model, so as to export the reduced-order model of heat flux as an FMU model. The FMU model complies with the FMI interface specification and can be used in other system simulation software, which is conducive to building the digital twin model system in Simulink in the subsequent steps of this embodiment.

[0072] (3.3) Build the digital twin model system.

[0073] (3.3.1) Temperature field monitoring digital twin model.

[0074] In Simulink, use the FMU Import module to import the FMU model file corresponding to the reduced-order model of the temperature field to generate a temperature field ROM (Reduced Order Model) module. Use the From Snapsheet module to import the data table generated by the sensor (including the real-time values of liquid phase height, liquid phase temperature, gas phase temperature, and outer wall temperature). Determine the temperature field through the temperature field ROM module, and use an oscilloscope to display the temperature field results. Connect each module through wires to build a digital twin simulation model, that is, the temperature field monitoring digital twin model, as Figure 6 shown.

[0075] (3.3.2) Pressure relief cycle prediction digital twin model.

[0076] In Simulink, use the FMU Import module to import the FMU model file corresponding to the reduced-order model of heat flux to generate a heat flux ROM module. Further combine it with the pressure relief cycle prediction module (including daily evaporation rate calculation and pressure relief cycle calculation). Use the From Snapsheet module to import the data table generated by the sensor (including the real-time values of liquid phase height, liquid phase temperature, gas phase temperature, outer wall temperature, and gas phase pressure). Determine the total heat flux of the outer wall through the heat flux ROM module, and predict the pressure relief cycle through the pressure relief cycle prediction module. Connect each module through wires to build a digital twin simulation model, that is, the pressure relief cycle prediction digital twin model, as Figure 7 shown.

[0077] In this embodiment, different modules such as sensor data acquisition, reduced-order model of heat flux, reduced-order model of temperature field, daily evaporation rate calculation, and pressure relief cycle calculation are connected to build a digital twin model system for cryogenic storage tanks. The digital twin model system includes a temperature field monitoring digital twin model and a pressure relief cycle prediction digital twin model. Subsequently, temperature field monitoring and pressure relief cycle prediction are completed based on the digital twin model system of this cryogenic storage tank.

[0078] At this time, in this embodiment, the establishment process of the temperature field reduced-order model and the heat flux reduced-order model includes the following steps.

[0079] (1) Construct a thermal simulation geometric parameterization model of the cryogenic storage tank.

[0080] Constructing a thermal simulation geometric parameterization model of the cryogenic storage tank specifically includes: constructing a geometric model of the cryogenic storage tank, the geometric model including an inner tank model, an outer tank model, and a vacuum interlayer model located between the inner tank model and the outer tank model; setting the geometric parameters and material parameters of the geometric model to obtain the thermal simulation geometric parameterization model of the cryogenic storage tank, the geometric parameters including the thickness, length, and inner diameter of the inner tank model and the thickness, length, and outer diameter of the outer tank model, and the material parameters including the first thermal conductivity of the inner tank model, the second thermal conductivity of the outer tank model, and the third thermal conductivity of the vacuum interlayer model.

[0081] Among them, constructing the geometric model of the cryogenic storage tank specifically includes: constructing a geometric model of a quarter of the cryogenic storage tank, the quarter of the cryogenic storage tank being the partial cryogenic storage tank determined by evenly dividing the cryogenic storage tank along the axial direction and the circumferential direction of the cryogenic storage tank. Specifically, evenly dividing along the axial direction of the cryogenic storage tank to obtain two half cryogenic storage tanks, and evenly dividing along the circumferential direction of one half cryogenic storage tank to obtain two quarter cryogenic storage tanks. The above division process is only an assumed division for clearly expressing the quarter cryogenic storage tank and is not an actual division of the cryogenic storage tank.

[0082] Among them, the first thermal conductivity is the thermal conductivity of the material used for the inner tank model, the second thermal conductivity is the thermal conductivity of the material used for the outer tank model, and the determination method of the third thermal conductivity includes: randomly determining a plurality of initial thermal conductivities; for each initial thermal conductivity, obtaining the experimental value of the total heat flux on the outer wall surface of the cryogenic storage tank determined through experiments under the simulation parameters, determining the initial thermal simulation geometric parameterization model corresponding to the initial thermal conductivity, setting the boundary conditions of the initial thermal simulation geometric parameterization model based on the simulation parameters to obtain the initial thermal simulation parameterization model, performing simulation calculations using the initial thermal simulation parameterization model to obtain the simulation value of the total heat flux on the outer wall surface of the cryogenic storage tank corresponding to the initial thermal conductivity, and selecting the initial thermal conductivity with the smallest difference between the experimental value and the simulation value of the total heat flux on the outer wall surface as the third thermal conductivity. The simulation parameters include the simulation values of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank.

[0083] If the minimum difference is not less than the preset difference, this embodiment selects a plurality of initial thermal conductivities again and performs calculations again until the minimum difference is less than the preset difference. At this time, the initial thermal conductivity with the smallest difference is selected as the third thermal conductivity.

[0084] (2) For each set of preset sample parameters, set the boundary conditions of the thermal simulation geometric parametric model based on the sample parameters to obtain the thermal simulation parametric model, and perform simulation calculations based on the thermal simulation parametric model to obtain the sample temperature field and the total heat flux of the outer wall surface of the cryogenic storage tank corresponding to the sample parameters. Among them, the sample parameters include the sample values of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank.

[0085] The determination method of the sample parameters includes: obtaining the value ranges of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank; using the Latin hypercube sampling method to perform multiple samplings within the value ranges of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank. Each sampling obtains a set of sample parameters, and multiple sets of sample parameters are obtained through multiple samplings.

[0086] Setting the boundary conditions of the thermal simulation geometric parametric model based on the sample parameters to obtain the thermal simulation parametric model specifically includes: determining the positions of the liquid and gas inside the inner tank model based on the sample value of the liquid phase height of the cryogenic storage tank, setting the outer surface temperature of the outer tank model as the sample value of the outer wall surface temperature of the cryogenic storage tank, setting the temperature of the contact surface between the inner tank model and the liquid inside the inner tank model as the sample value of the liquid phase temperature of the cryogenic storage tank, and setting the temperature of the contact surface between the inner tank model and the gas inside the inner tank model as the sample value of the gas phase temperature of the cryogenic storage tank.

[0087] (3) Fit all sets of sample parameters and the sample temperature fields corresponding to each set of sample parameters to generate a reduced-order temperature field model, and fit all sets of sample parameters and the total heat flux of the outer wall surface corresponding to each set of sample parameters to generate a reduced-order heat flux model.

[0088] Fitting all sets of sample parameters and the sample temperature fields corresponding to each set of sample parameters to generate a reduced-order temperature field model, and fitting all sets of sample parameters and the total heat flux of the outer wall surface corresponding to each set of sample parameters to generate a reduced-order heat flux model specifically includes: using all sets of sample parameters and the sample temperature fields corresponding to each set of sample parameters as inputs, and using the static reduced-order model method for fitting to generate a reduced-order temperature field model; using all sets of sample parameters and the total heat flux of the outer wall surface corresponding to each set of sample parameters as inputs, and using the response surface method for fitting to generate a reduced-order heat flux model.

[0089] (2) Real-time collect the operation data of the cryogenic storage tank through sensors.

[0090] In this embodiment, the operation data of the cryogenic storage tank is real-time collected through sensors and uploaded to the cloud as the input data of the reduced-order temperature field model and the reduced-order heat flux model. The operation data includes: liquid phase height, liquid phase temperature, gas phase temperature, outer wall surface temperature, and gas phase pressure.

[0091] (3) Calculate the daily evaporation rate based on the total heat flux on the outer wall surface calculated in real time according to the heat flux degradation model and the latent heat of vaporization of the medium. Using the daily evaporation rate and the gas phase pressure, predict the pressure relief period by means of the ideal gas state equation.

[0092] Since the total heat flux on the outer wall surface is output by the heat flux degradation model, it is necessary to calculate the daily evaporation rate. In the digital twin model system, the MATLAB Function module can be used to calculate the daily evaporation rate.

[0093] The calculation formula for the daily evaporation rate is shown in the following formula (1).

[0094] (1).

[0095] In formula (1), is the daily evaporation rate of the medium, with the unit of % / d (i.e., day); is the total heat flux on the outer wall surface in real time, with the unit of J / S; is the latent heat of vaporization of the medium, with the unit of J / kg; is the density of the medium, with the unit of kg / m 3 ; is the effective volume of the cryogenic storage tank, with the unit of m 3 .

[0096] Based on the daily evaporation rate of the cryogenic storage tank, conservatively considering that the gas phase volume of the cryogenic storage tank remains unchanged, use the ideal gas state equation to derive the calculation formula for the pressure relief period of the cryogenic storage tank. The calculation formula for the pressure relief period is shown in the following formula (2). In the digital twin model system, the calculation process of formula (2) can be implemented in Simulink using the MATLAB Function module to predict the pressure relief period.

[0097] The calculation formula for the real-time pressure relief period is shown in the following formula (2).

[0098] (2).

[0099] In formula (2), is the real-time pressure relief period of the gas, with the unit of d; is the relief pressure, with the unit of Pa; is the real-time monitored value of the gas phase pressure of the cryogenic storage tank, with the unit of Pa; is the real-time gas phase volume of the cryogenic storage tank, with the unit of m 3 ; is the gas molecular mass corresponding to the medium, with the unit of g / mol. For example, if the medium is liquid nitrogen, then it is the gas molecular mass of nitrogen at this time; is the gas constant, with a value of 8.31 J / (mol·K); is the real-time monitored value of the gas phase temperature of the cryogenic storage tank, with the unit of K.

[0100] At this time, in this embodiment, the real-time monitored values of the liquid level height, liquid phase temperature, gas phase temperature, outer wall temperature and gas phase pressure of the cryogenic storage tank are obtained. Taking the real-time monitored values of the liquid level height, liquid phase temperature, gas phase temperature and outer wall temperature of the cryogenic storage tank as inputs, the real-time temperature field of the cryogenic storage tank is determined by using the temperature field reduction model, and the real-time total heat flux of the outer wall of the cryogenic storage tank is determined by using the heat flux reduction model. Based on the real-time monitored value of the gas phase pressure of the cryogenic storage tank and the real-time total heat flux of the outer wall, the real-time pressure relief period of the cryogenic storage tank is calculated.

[0101] Based on the real-time monitored value of the gas phase pressure of the cryogenic storage tank and the real-time total heat flux of the outer wall, the real-time pressure relief period of the cryogenic storage tank is calculated, specifically including: based on the real-time total heat flux of the outer wall, the daily evaporation rate is calculated, specifically calculated by formula (1), and based on the real-time monitored value of the gas phase pressure of the cryogenic storage tank and the daily evaporation rate, the real-time pressure relief period of the cryogenic storage tank is calculated, specifically calculated by formula (2).

[0102] The method for determining the real-time gas phase volume of the cryogenic storage tank includes: taking the real-time monitored value of the liquid level height of the cryogenic storage tank as an input, and using the gas phase volume reduction model to determine the real-time gas phase volume of the cryogenic storage tank. The establishment process of the gas phase volume reduction model includes: obtaining multiple sample liquid level heights. For each sample liquid level height, the sample gas phase volume of the cryogenic storage tank corresponding to the sample liquid level height is determined by means of three-dimensional modeling (i.e., the modeling method in three-dimensional modeling software. Specifically, in the three-dimensional modeling software, based on the sample liquid level height, gas is filled in the inner tank model, and then the gas volume can be determined), and fitting all the sample liquid level heights and the sample gas phase volumes corresponding to each sample liquid level height to generate the gas phase volume reduction model.

[0103] Among them, fitting all the sample liquid level heights and the sample gas phase volumes corresponding to each sample liquid level height to generate the gas phase volume reduction model specifically includes: taking all the sample liquid level heights and the sample gas phase volumes corresponding to each sample liquid level height as inputs, and using the response surface method for fitting to generate the gas phase volume reduction model.

[0104] This embodiment provides a method for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank based on digital twin, including the following steps: establishing a thermal simulation parametric model of the cryogenic storage tank, generating training samples using DOE experiments, establishing a reduced-order model of the temperature field using the static reduced-order model method, establishing a reduced-order model of the heat flux using the response surface method to reduce the computational complexity, collecting the liquid level height, liquid phase temperature, gas phase temperature, outer wall temperature, and gas phase pressure of the cryogenic storage tank in real time through sensors, generating the temperature field through the reduced-order model of the temperature field to complete the temperature field monitoring, generating the total heat flux of the outer wall through the reduced-order model of the heat flux, calculating the daily evaporation rate based on the total heat flux of the outer wall and the latent heat of vaporization of the medium, and further predicting the pressure relief cycle using the ideal gas state equation to complete the pressure relief cycle prediction, realizing the real-time monitoring of the temperature field and the real-time prediction of the pressure relief cycle, solving the problems of long time consumption, poor real-time performance of the numerical simulation method, and low accuracy of the empirical formula method, and being able to effectively improve the safety and operation efficiency of the cryogenic storage tank and reduce the loss of cryogenic liquid and cost caused by the pressure relief operation.

[0105] This embodiment uses digital twin technology to establish a reduced-order model of the temperature field and a reduced-order model of the heat flux of the cryogenic storage tank, and constructs a calculation formula for the daily evaporation rate and a calculation formula for the pressure relief cycle. Therefore, the temperature field, total heat flux of the outer wall, daily evaporation rate, and pressure relief cycle of the cryogenic storage tank can be calculated quickly and in real time, overcoming the disadvantages of long time consumption, poor real-time performance of the traditional numerical simulation method, and inaccurate calculation of the empirical formula method.

[0106] This application also provides an application scenario that applies the above-mentioned method for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank. Specifically, the method for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank provided in this embodiment can be applied in the operation scenario of a cryogenic storage tank. The operation scenario of a cryogenic storage tank includes a prediction link and an operation link. The prediction link is used to determine the temperature field and pressure relief cycle of the cryogenic storage tank, and the operation link is used to control the operation of the cryogenic storage tank based on the temperature field and pressure relief cycle. The method for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank provided in this embodiment belongs to the prediction link.

[0107] Embodiment 2.

[0108] This embodiment provides a device for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank. The device for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank includes: a processor and a liquid level sensor, a first temperature sensor, a second temperature sensor, a third temperature sensor, and a pressure sensor that are communicatively connected to the processor.

[0109] A liquid level sensor, a first temperature sensor, a second temperature sensor, a third temperature sensor, and a pressure sensor are all installed on the cryogenic storage tank. The liquid level sensor is used to collect the real-time monitoring value of the liquid phase height of the cryogenic storage tank. The first temperature sensor is used to collect the real-time monitoring value of the liquid phase temperature of the cryogenic storage tank. The second temperature sensor is used to collect the real-time monitoring value of the gas phase temperature of the cryogenic storage tank. The third temperature sensor is used to collect the real-time monitoring value of the outer wall temperature of the cryogenic storage tank. The pressure sensor is used to collect the real-time monitoring value of the gas phase pressure of the cryogenic storage tank.

[0110] The processor is used to obtain the real-time monitoring values of the liquid phase height, liquid phase temperature, gas phase temperature, outer wall temperature, and gas phase pressure of the cryogenic storage tank, execute the cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method described in Embodiment 1, and determine the real-time temperature field and real-time pressure relief cycle of the cryogenic storage tank.

[0111] Embodiment 3.

[0112] In an exemplary embodiment, a computer device is provided. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method.

[0113] Those skilled in the art can understand that Figure 8 the structure shown in

[0114] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the method for monitoring the temperature field of the cryogenic storage tank and predicting the pressure relief cycle in Embodiment 1 is implemented.

[0115] Embodiment 4.

[0116] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the method for monitoring the temperature field of the cryogenic storage tank and predicting the pressure relief cycle in Embodiment 1 is implemented.

[0117] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. And the collection, use, and processing of relevant data need to comply with relevant regulations.

[0118] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0119] In this article, specific examples are used to elaborate on the principle and implementation manner of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A method for monitoring the temperature field and predicting the pressure relief cycle of a cryogenic storage tank, characterized in that The method for monitoring the temperature field and predicting the pressure relief period of a cryogenic storage tank includes: Obtaining real-time monitoring values of the liquid level height, liquid phase temperature, gas phase temperature, outer wall temperature, and gas phase pressure of the cryogenic storage tank; Taking the real-time monitoring values of the liquid level height, liquid phase temperature, gas phase temperature, and outer wall temperature of the cryogenic storage tank as inputs, using a temperature field reduced-order model to determine the real-time temperature field of the cryogenic storage tank, and using a heat flux reduced-order model to determine the total real-time heat flux of the outer wall of the cryogenic storage tank; Calculating the real-time pressure relief period of the cryogenic storage tank based on the real-time monitoring value of the gas phase pressure of the cryogenic storage tank and the total real-time heat flux of the outer wall; Wherein, the establishment process of the temperature field reduced-order model and the heat flux reduced-order model includes: constructing a thermal simulation geometric parameterization model of the cryogenic storage tank; for each set of preset sample parameters, setting the boundary conditions of the thermal simulation geometric parameterization model based on the sample parameters to obtain a thermal simulation parameterization model, and performing simulation calculations based on the thermal simulation parameterization model to obtain the sample temperature field and the total sample heat flux of the outer wall of the cryogenic storage tank corresponding to the sample parameters; fitting all sets of the sample parameters and the sample temperature fields corresponding to each set of the sample parameters to generate the temperature field reduced-order model, and fitting all sets of the sample parameters and the total sample heat fluxes of the outer wall corresponding to each set of the sample parameters to generate the heat flux reduced-order model; the sample parameters include the sample values of the liquid level height, liquid phase temperature, gas phase temperature, and outer wall temperature of the cryogenic storage tank.

2. The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to claim 1, wherein, Constructing a thermal simulation geometric parameterization model of the cryogenic storage tank specifically includes: Constructing a geometric model of the cryogenic storage tank; the geometric model includes an inner tank model, an outer tank model, and a vacuum interlayer model located between the inner tank model and the outer tank model; Setting the geometric parameters and material parameters of the geometric model to obtain a thermal simulation geometric parameterization model of the cryogenic storage tank; the geometric parameters include the thickness, length, and inner diameter of the inner tank model and the thickness, length, and outer diameter of the outer tank model; the material parameters include the first thermal conductivity of the inner tank model, the second thermal conductivity of the outer tank model, and the third thermal conductivity of the vacuum interlayer model.

3. The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to claim 2, wherein, Constructing a geometric model of the cryogenic storage tank specifically includes: constructing a geometric model of a quarter of the cryogenic storage tank; the quarter cryogenic storage tank is a partial cryogenic storage tank determined by equally dividing the cryogenic storage tank along its axial and circumferential directions; The first thermal conductivity is the thermal conductivity of the material used for the inner tank model; The second thermal conductivity is the thermal conductivity of the material used for the outer tank model; The method for determining the third thermal conductivity includes: randomly determining a plurality of initial thermal conductivities; for each of the initial thermal conductivities, obtaining an experimental value of the total heat flux on the outer wall surface of the cryogenic storage tank determined through experiments under simulation parameters, determining an initial thermal simulation geometric parameterized model corresponding to the initial thermal conductivity, setting boundary conditions for the initial thermal simulation geometric parameterized model based on the simulation parameters to obtain an initial thermal simulation parameterized model, using the initial thermal simulation parameterized model for simulation calculation to obtain a simulation value of the total heat flux on the outer wall surface of the cryogenic storage tank corresponding to the initial thermal conductivity, and selecting the initial thermal conductivity with the smallest difference between the experimental value and the simulation value of the total heat flux on the outer wall surface as the third thermal conductivity; the simulation parameters include simulation values of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank.

4. The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to claim 1, wherein, The method for determining the sample parameters includes: obtaining the value ranges of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank; using the Latin hypercube sampling method to perform multiple samplings within the value ranges of the liquid phase height, liquid phase temperature, gas phase temperature, and outer wall surface temperature of the cryogenic storage tank, and obtaining a set of sample parameters for each sampling.

5. The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to claim 2, characterized in that Setting boundary conditions for the thermal simulation geometric parameterized model based on the sample parameters to obtain a thermal simulation parameterized model specifically includes: determining the positions of the liquid and gas inside the inner tank model based on the sample value of the liquid phase height of the cryogenic storage tank, setting the outer surface temperature of the outer tank model as the sample value of the outer wall surface temperature of the cryogenic storage tank, setting the temperature of the contact surface between the inner tank model and the liquid inside the inner tank model as the sample value of the liquid phase temperature of the cryogenic storage tank, and setting the temperature of the contact surface between the inner tank model and the gas inside the inner tank model as the sample value of the gas phase temperature of the cryogenic storage tank.

6. The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to claim 1, characterized in that Fitting all groups of the sample parameters and the sample temperature fields corresponding to each group of the sample parameters to generate the temperature field reduced-order model, and fitting all groups of the sample parameters and the sample total heat fluxes on the outer wall surface corresponding to each group of the sample parameters to generate the heat flux reduced-order model, specifically including: Using the static reduced-order model method to fit with all groups of the sample parameters and the sample temperature fields corresponding to each group of the sample parameters as inputs to generate the temperature field reduced-order model; Using the response surface method to fit with all groups of the sample parameters and the sample total heat fluxes on the outer wall surface corresponding to each group of the sample parameters as inputs to generate the heat flux reduced-order model.

7. The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to claim 1, wherein, Calculating the real-time pressure relief period of the cryogenic storage tank based on the real-time monitoring value of the gas phase pressure of the cryogenic storage tank and the real-time total heat flux on the outer wall surface, specifically including: Calculating the daily evaporation rate based on the real-time total heat flux on the outer wall surface; Calculating the real-time pressure relief period of the cryogenic storage tank based on the real-time monitoring value of the gas phase pressure of the cryogenic storage tank and the daily evaporation rate; Wherein, the calculation formula for the daily evaporation rate is: ； wherein, is the daily evaporation rate of the medium; is the total heat flux of the real-time outer wall surface; is the latent heat of vaporization of the medium; is the density of the medium; is the effective volume of the cryogenic storage tank; The calculation formula for the real-time pressure relief period is: ； Among them, is the real-time pressure relief period of the gas; is the relief pressure; is the real-time monitored value of the gas phase pressure of the cryogenic storage tank; is the real-time gas phase volume of the cryogenic storage tank; is the gas molecular mass corresponding to the medium; is the gas constant; is the real-time monitored value of the gas phase temperature of the cryogenic storage tank.

8. The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to claim 7, wherein, The method for determining the real-time gas phase volume of a cryogenic storage tank includes: using the real-time monitoring value of the liquid level height of the cryogenic storage tank as an input, and determining the real-time gas phase volume of the cryogenic storage tank by using a reduced-order gas phase volume model; wherein, the process of establishing the reduced-order gas phase volume model includes: obtaining a plurality of sample liquid level heights, and for each of the sample liquid level heights, determining the sample gas phase volume of the cryogenic storage tank corresponding to the sample liquid level height by means of three-dimensional modeling; fitting all the sample liquid level heights and the sample gas phase volume corresponding to each sample liquid level height to generate the reduced-order gas phase volume model; Among them, fitting all the sample liquid level heights and the sample gas phase volume corresponding to each sample liquid level height to generate the reduced-order gas phase volume model specifically includes: using all the sample liquid level heights and the sample gas phase volume corresponding to each sample liquid level height as inputs, and performing fitting by using the response surface method to generate the reduced-order gas phase volume model.

9. A cryogenic storage tank temperature field monitoring and pressure relief cycle prediction device, characterized in that, The cryogenic storage tank temperature field monitoring and pressure relief cycle prediction device includes: a processor and a liquid level sensor, a first temperature sensor, a second temperature sensor, a third temperature sensor, and a pressure sensor that are communicatively connected to the processor; The liquid level sensor, the first temperature sensor, the second temperature sensor, the third temperature sensor, and the pressure sensor are all installed on the cryogenic storage tank. The liquid level sensor is used to collect the real-time monitoring value of the liquid level height of the cryogenic storage tank. The first temperature sensor is used to collect the real-time monitoring value of the liquid phase temperature of the cryogenic storage tank. The second temperature sensor is used to collect the real-time monitoring value of the gas phase temperature of the cryogenic storage tank. The third temperature sensor is used to collect the real-time monitoring value of the outer wall surface temperature of the cryogenic storage tank. The pressure sensor is used to collect the real-time monitoring value of the gas phase pressure of the cryogenic storage tank; The processor is used to obtain the real-time monitoring values of the liquid level height, liquid phase temperature, gas phase temperature, outer wall surface temperature, and gas phase pressure of the cryogenic storage tank, execute the cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to any one of claims 1-8, and determine the real-time temperature field and real-time pressure relief cycle of the cryogenic storage tank.

10. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the cryogenic storage tank temperature field monitoring and pressure relief cycle prediction method according to any one of claims 1-8.

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